Biology subjects

Le Cam, L.

Publications and source records attributed to Le Cam, L..

7 recordsLinked to original sources

bioRxiv2026

THE MULTIFUNCTIONAL PROTEIN E4F1 REGULATES MELANOCYTIC FUNCTIONS IN MICE THROUGH PROTEIN TRANSLATION AND MITF EXPRESSION

The multifunctional protein E4 transcription factor 1 (E4F1) is an essential regulator of skin homeostasis, playing a critical role in epidermal stem cell maintenance and keratinocyte differentiation. However, the functions of E4F1 in other skin cell type remain poorly investigated. Here, we describe its role in melanogenesis, the physiological process for synthesis of melanin pigments. Using genetically engineered mouse models that enabled conditional inactivation of E4f1 specifically in melanocytes, we demonstrated that E4f1 loss induces severe pigmentation defects without affecting melanocyte survival nor differentiation. At the molecular level, E4f1 depletion reduces the expression of its direct transcriptional target genes involved in protein translation, including Elp3 and Eef1g, leading to translation alterations and consequently the induction of the Integrated Stress Response (ISR). This response resulted in microphthalmia-associated transcription factor (MITF) downregulation, inducing a decrease in the expression of enzymes involved in melanin synthesis. Consistent with these findings, Elp3 or Eef1g depletion recapitulated the decrease in MITF levels in vitro, and pharmacological inhibition of ISR partially rescued melanin content in vivo, confirming a key role of the E4F1-protein translation-ISR cascade in melanogenesis. Together, our data identify E4F1-associated translation functions as a key regulator of the melanocytic lineage.

physiology↗

bioRxiv2026

Androgen Receptor mediates beige adipocyte homeostasis and plasticity

Male adipose tissue undergoes profound post-pubertal remodeling characterized by the progressive transition of beige adipocytes toward a white adipocyte phenotype. Although androgen signaling has been implicated in adipose tissue biology, its role in beige adipocyte remodeling and metabolic plasticity remains poorly understood. Here we show that androgen receptor (AR) signaling is dynamically activated in inguinal white adipose tissue during the post-pubertal beige-to-white transition in male mice. Inducible deletion of AR in beige adipocytes impaired this remodeling process, resulting in the persistence of multilocular beige-like adipocytes despite reduced thermogenic competence and marked mitochondrial abnormalities. Transcriptomic and cistromic analyses identified AR as a direct regulator of adipocyte metabolic and differentiation programs, whereas immune-related transcriptional signatures in AR-deficient adipose tissue primarily reflected macrophage infiltration and inflammatory remodeling. Loss of AR promoted mitochondrial dysfunction, mitophagy, and altered glucose handling, while cell-autonomous AR silencing in beige adipocytes recapitulated key defects in mitochondrial organization and adipocyte identity. Longitudinal and metabolic challenge studies further demonstrated that AR signaling is required for age-associated adipose remodeling and adaptive beige adipocyte plasticity during high-fat diet feeding and cold exposure. Together, these findings identify AR as a central regulator of beige adipocyte remodeling, mitochondrial homeostasis, and adaptive metabolic function in male adipose tissue.

physiology↗

bioRxiv2024

Acute dietary methionine restriction highlights sensitivity of neocortex development to metabolic variations

Methionine -an essential amino acid that has to be provided by nutrition- and its metabolite S-Adenosyl methionine (SAM) are indispensable for cell proliferation, stem cell maintenance and epigenetic regulation 1-5, three processes that are central to embryonic development 6. Previous studies using chronic dietary restriction of methyl donors prior to and during gestation indicated that methionine restriction (MR) is detrimental to the development or growth of the neocortex 7,8, however, the consequences of acute MR have not been extensively studied. Here, we designed a dietary MR regime coinciding with the neurogenic phases of neocortex development in the mouse. Our results indicate that dietary MR for 5 days leads to a severe reduction in neocortex growth and neuronal production. In comparison, growth of the liver and heart was unaffected, highlighting an organ-specific response to MR which was also observed at the cellular and molecular levels. Progenitor cohort labeling revealed a time-dependent sensitivity to MR and cell cycle analyses indicated that after 5 days of MR, progenitors are stalled in the S/G2 phases. Unexpectedly, neocortex growth reduction induced after 5 days of MR is completely rescued at birth when switching the dam back to control diet for the remaining of gestation, uncovering a mechanism of catch-up growth. Using multiplexed imaging we probed metabolic and epigenetic markers following MR and during catch-up growth and show that pyruvate metabolism is rewired in progenitors. Altogether, our data uncover a transient state of quiescence in G2/S which is metabolically distinct from G0 quiescence and associated with efficient catch-up growth. More globally, our study highlights both the extreme sensitivity of the developing neocortex to acute dietary changes and its remarkable plasticity.

developmental biology↗

bioRxiv2024

Deciphering Oxygen Distribution and Hypoxia Profiles in the Tumor Microenvironment: A Data-Driven Mechanistic Modeling Approach

The distribution of hypoxia within tissues plays a critical role in tumor diagnosis and prognosis. Recognizing the significance of tumor oxygenation and hypoxia gradients, we introduce mathematical frameworks grounded in mechanistic modeling approaches for their quantitative assessment within a tumor microenvironment. Our approach provides a non-invasive method to measure and predict hypoxia using known blood vasculature. Formulating a reaction-diffusion model for oxygen distribution, we apply it to derive the corresponding hypoxia profile. The modeling and simulations successfully replicate the observed inter- and intra-tumor heterogeneity in experimentally obtained hypoxia profiles across various tumor tissues (breast, ovarian, and pancreatic) in our dataset. Employing a data-driven approach, we propose a method to deduce partial differential equation (PDE) models with spatially dependent parameters, enabling us to comprehend the variability of hypoxia profiles within a tissue. The versatility of our framework lies not only in capturing diverse and dynamic behaviors of tumor oxygenation but also in categorizing states of vascularization. These categories are distinguished based on the dynamics of oxygen molecules, as identified by the model parameters.

cancer biology↗

bioRxiv2022

Computational Model of Heterogeneity in Melanoma: Designing Therapies and Predicting Outcomes

Cutaneous melanoma is a highly invasive tumor and, despite the development of recent therapies, most patients with advanced metastatic melanoma have a poor clinical outcome. The most frequent mutations in melanoma affect the BRAF oncogene, a protein kinase of the MAPK signaling pathway. Therapies targeting both BRAF and MEK are effective for only 50% of patients and, almost systematically, generate drug resistance. Genetic and non-genetic mechanisms associated with the strong heterogeneity and plasticity of melanoma cells have been suggested to favor drug resistance but are still poorly understood. Recently, we have introduced a novel mathematical formalism allowing the representation of the relation between tumor heterogeneity and drug resistance and proposed several models for the development of resistance of melanoma treated with BRAF/MEK inhibitors. In this paper, we further investigate this relationship by using a new computational model that copes with multiple cell states identified by single cell mRNA sequencing data in melanoma treated with BRAF/MEK inhibitors. We use this model to predict the outcome of different therapeutic strategies. The reference therapy, referred to as "continuous" consists in applying one drug (or several drugs) without disruption. In "combination therapy", several drugs are used sequentially. In "adaptive therapy" drug application is interrupted when the tumor size is below a lower threshold and resumed when the size goes over an upper threshold. We show that, counter-intuitively, the optimal protocol in combination therapy of BRAF/MEK inhibitors with a hypothetical drug targeting cell states that develop later during the tumor response to kinase inhibitors, is to treat first with this hypothetical drug. Also, even though there is little difference in the timing of emergence of the resistance between continuous and adaptive therapies, the spatial distribution of the different melanoma subpopulations is more zonated in the case of adaptive therapy.

systems biology↗

bioRxiv2020

Diversity-based enumeration of optimal context-specific metabolic networks

The correct identification of metabolic activity in tissues or cells under different environmental or genetic conditions can be extremely elusive due to mechanisms such as post-transcriptional modification of enzymes or different rates in protein degradation, making difficult to perform predictions on the basis of gene expression alone. Context-specific metabolic network reconstruction can overcome these limitations by leveraging the integration of multi-omics data into genome-scale metabolic networks (GSMN). Using the experimental information, context-specific models are reconstructed by extracting from the GSMN the sub-network most consistent with the data, subject to biochemical constraints. One advantage is that these context-specific models have more predictive power since they are tailored to the specific organism and condition, containing only the reactions predicted to be active in such context. A major limitation of this approach is that the available information does not generally allow for an unambiguous characterization of the corresponding optimal metabolic sub-network, i.e., there are usually many different sub-network that optimally fit the experimental data. This set of optimal networks represent alternative explanations of the possible metabolic state. Ignoring the set of possible solutions reduces the ability to obtain relevant information about the metabolism and may bias the interpretation of the true metabolic state. In this work, we formalize the problem of enumeration of optimal metabolic networks, we implement a set of techniques that can be used to enumerate optimal networks, and we introduce DEXOM, a novel strategy for diversity-based extraction of optimal metabolic networks. Instead of enumerating the whole space of optimal metabolic networks, which can be computationally intractable, DEXOM samples solutions from the set of optimal metabolic sub-networks maximizing diversity in order to obtain a good representation of the possible metabolic state. We evaluate the solution diversity of the different techniques using simulated and real datasets, and we show how this method can be used to improve in-silico gene essentiality predictions in Saccharomyces Cerevisiae using diversity-based metabolic network ensembles. Both the code and the data used for this research are publicly available on GitHub1.

bioinformatics↗

bioRxiv2018

Spatio-Genetic and Phenotypic Modelling Elucidates Resistance and Re-Sensitisation to Treatment in Heterogeneous Melanoma

Although novel targeted therapies have significantly improved the overall survival of patients with advanced melanoma, understanding and combatting drug resistance remains a major clinical challenge. Using partial differential equations, we describe the evolution of a cellular population through time, space, and phenotype dimensions, in the presence of various drug species. We then use this framework to explore models in which resistance is attained by either mutations (irreversible) or plasticity (reversible). Numerical results suggest that punctuated evolutionary assumptions are more consistent with results obtained from murine melanoma models than gradual evolution. Furthermore, in the context of an evolving tumour cell population, sequencing the treatment, for instance applying immunotherapy before BRAF inhibitors, can increase treatment effectiveness. However, drug strategies which showed success within a spatially homogeneous tumour environment were unsuccessful under heterogeneous conditions, suggesting that spatio-environmental heterogeneity may be the greatest challenge to tumour therapies. Plastic metabolic models are additionally capable of reproducing the characteristic resistant tumour volume curves and predicting re-sensitisation to secondary waves of treatment observed in patient derived xenograft (PDX) melanomas treated with MEK and BRAF inhibitors. Nevertheless, secondary relapse due to a pre-adapted subpopulation, remaining after the first wave of treatment, results in a more rapid development of resistance. Our model provides a framework through which tumour resistance can be understood and would suggest that carefully phased treatments may be able to overcome the development of long-term resistance in melanoma.

cancer biology↗